For years, the goal in neurodegenerative disease has been to find a definitive test that can identify what is wrong, predict what will happen next, and guide treatment. That ambition is understandable, but a single test is unlikely to provide all these answers.
Biomarkers have transformed the study of neurodegenerative disease. We can now detect biological changes that were once visible only after death. Blood-based tests are making this information more accessible, while imaging can reveal pathological processes in the brain. However, detecting pathology is not the same as fully explaining disease.
A positive biomarker is an important data point, but it cannot fully explain why one patient develops memory loss while another experiences movement problems, sleep disruption, or hallucinations. Nor can a single result reliably predict disease progression or determine which treatment will provide the greatest benefit.
The future of neurodegenerative disease diagnostics therefore lies not in finding one biomarker that answers every question, but in interpreting complementary measures together to build a more complete and clinically meaningful picture of each patient.
The limits of a single answer
The complexity of neurodegenerative disease challenges conventional diagnostic approaches. Similar symptoms can arise from different biological processes, while variations in the location and progression of pathology can produce markedly different cognitive, motor, behavioral, or sensory symptoms.
Traditional labels such as Alzheimer’s disease and Parkinson’s disease remain clinically useful, but they do not always represent distinct biological categories. A patient may have amyloid and tau pathology alongside alpha-synuclein, TDP-43, or other pathological changes. These combinations may help explain why patients with the same diagnosis can follow very different clinical courses.
Rather than expecting one test to resolve this complexity, we should ask a more useful question: Which combination of measures provides the clearest picture of this patient’s disease?
Multimodal means complementary
Multimodal assessment should not involve collecting every available piece of data. Instead, it should integrate complementary evidence to answer specific clinical questions. Molecular biomarkers provide information about disease biology, imaging reveals changes in the brain or other tissues, and clinical assessments show how those changes affect the patient. Digital measures can add to this picture by tracking function between clinic visits. Each offers a different perspective, and their greatest value comes from interpreting them together.
This is particularly important because a patient’s trajectory may be more informative than a single result. A digital assessment completed at home cannot replace an in-person neurological examination, but it may reveal changes in speech, mobility, cognition, or behavior relative to the patient’s previous baseline. Longitudinal data can therefore turn isolated measurements into a meaningful clinical narrative.
The pathologist’s role is expanding
For pathologists, multimodal assessment should expand our role rather than diminish it. Because brain tissue is rarely available during life, laboratory evidence is particularly valuable in neurodegenerative disease. This places greater responsibility on us to explain clearly what a result can – and cannot – tell clinicians.
A laboratory result should be part of a broader diagnostic discussion involving neurology, radiology, pathology, and digital data systems. Molecular testing may identify a biological feature, imaging can show where disease-related changes are occurring, and clinical evaluation reveals how they affect the patient. The challenge is not simply to generate these results, but to make them useful when interpreted together.
Achieving this will require stronger data infrastructure, workflows that connect specialties, and reports that support interpretation and multidisciplinary decision-making rather than provide only a binary answer. Artificial intelligence may help identify patterns across large, complex datasets, but it is not a complete solution. An algorithm cannot compensate for poorly selected measurements, limited evidence, or unresolved biological questions.
From labels to individuals
Multimodal assessment will not make neurodegenerative disease simple. Its implementation will require validation, education, equitable access, interoperability, and changes to clinical workflows. However, relying on a single measurement to explain a heterogeneous, progressive disease is no longer sufficient.
Instead of asking which test will provide the answer, we should ask which combination of evidence can identify the underlying biology, show how it affects function, track changes over time, and guide the next clinical decision. The next era of neurodegenerative diagnostics will depend on our ability to turn complementary measurements into a coherent picture of the individual patient.
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